Multi-layered credit card with transaction-dependent source selection

ABSTRACT

Techniques are described herein for selecting an optimal financial account for a financial transaction. In an embodiment, a multi-account payment card is used to initiate a financial transaction. Transaction information of the financial transaction including a multi-account payment card ID is transmitted to a server for processing. The server determines that the multi-account payment card ID is associated with a plurality of financial accounts, wherein each of the plurality of financial accounts is associated with any one of a credit card, a debit card, an automatic teller machine (ATM) card, a gift card, or a credit line. A financial account of the plurality of financial accounts is selected by the server based on financial account information, such as anomalous transactions, associated with the plurality of financial accounts and the transaction information of the financial transaction. The financial transaction is then charged to the selected financial account.

CROSS-REFERENCE TO RELATED APPLICATIONS; BENEFIT CLAIM

This application claims the benefit as a Continuation of Appln. Ser. No.16/729,245, filed Dec. 27, 2019, the entire contents of which is herebyincorporated by reference as if fully set forth herein, under 35 U.S.C.§ 120. The applicant hereby rescinds any disclaimer of claim scope inthe parent application or the prosecution history thereof and advise theUSPTO that the claims in this application may be broader than any claimin the parent application.

FIELD OF THE INVENTION

The technical field to which the present disclosure generally relates iscomputer software in the field financial transaction processing. Thetechnical field also includes machine learning algorithms.

BACKGROUND

A consumer may use many types of payment cards such as credit cards,debit cards, and gift cards to make purchases. Each card may beassociated with financial accounts that have different transaction-basedrewards and/or different interest rates. For example, a user may havethe following four credit cards:

Card 1 Card2 Card3 Card4 Credit rate 15% 15% 10% 18% Travel-relatedpoints-per-dollar 3 0 0 0 Dining-related points-per-dollar 0 2 0 0 If$1000 is spent in first month 0 0 0 60,000 points

Consumers may find carrying and using multiple payment cardsinconvenient and burdensome. Additionally, consumers may experiencedifficulty identifying which card is the best card to use for eachtransaction to optimize their credit card rewards and interest rates.For example, among the four cards illustrated above, the best choice maybe to use Card4 until $1,000 is spent, and then to use Card1 fortravel-related expenses, Card2 for dining-related expenses, and Card3for everything else.

It is unlikely that a user is going to remember the benefit details ofeach card. Therefore, it is unlikely that a user will make the optimalchoice for each transaction. For example, a user may remember that Card3has the lowest interest rate, and therefore use Card3 for all purchases.Alternatively, the user may remember that it is important to use Card4to get the introductory bonus points. Under these circumstances, theuser may continue to use Card4 long after the $1000 spend requirementhas been satisfied, thereby losing the benefits of the other cards.Thus, techniques are desired to automatically select, on aper-financial-transaction basis, an optimal payment card out of a set ofpayment cards.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is a block diagram illustrating a system fortransaction-dependent financial account selection, according to anembodiment.

FIG. 2 is a flowchart illustrating steps for transaction-dependentfinancial account selection, according to an embodiment.

FIG. 3 is a block diagram of a computer system that may be used toimplement the techniques described herein.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

General Overview

Techniques are described herein for linking a “multi-account paymentcard” of a consumer with multiple financial accounts of the consumer.The accounts to which the multi-account payment card is linked mayinclude, for example, credit card accounts, automated teller machine(ATM) debit card accounts, gift card accounts, credit line accounts,and/or investment accounts. A multi-account payment card can be, forexample, a magnetic stripe card physically similar to a traditionalcredit card, a smartcard (i.e., a card with embedded IC chip), a cardwith a bar code, or a card with a QR code. Each financial account may beassociated with different rewards and interest rates.

A consumer can engage in a financial transaction with a merchant usingthe multi-account payment card. A financial transaction referred toherein can be, for example, point-of-sale (POS) transaction where theconsumer makes a purchase at a store front, or other “brick-and-mortar”location, or simply in the presence of a merchant, or an electronictransaction conducted over the Internet (e.g., enter card number of themulti-account payment card using a web site).

A consumer can use the multi-account payment card to make a payment of afinancial transaction in which funds for the payment come from one ofthe linked financial accounts. Based on (a) financial accountinformation associated with each of the financial accounts and (b)transaction information associated with the financial transaction, atransaction-dependent financial account selection system (“TDFASsystem”) automatically selects a financial account from one of thelinked financial accounts in an optimum manner (e.g. to maximize rewardpoints and/or minimize interest payments).

For example, reward information for a first financial account mayspecify that the first financial account offers 3 points per dollar ontravel purchases, 3 points per dollar on dining purchases, 1 point perdollar everything else, and no foreign transaction fees. A secondfinancial account may specify that the second financial account offers 5points per dollar on travel purchases and 1 point per dollar everythingelse. If transaction information is received for a financial transactionthat indicates a travel related purchase, the second financial accountthat maximizes points for travel purchases may be selected. Similarly,if transaction information is received for a financial transaction thatindicates a dining related purchase, the first financial account thatmaximizes points for dining purchase may be selected.

Feedback information for a financial account selection can be providedby a consumer after a financial account is selected for a financialtransaction. Such feedback information can be used to tune machinelearning models that may be used in combination with other selectiontechniques to provide an optimal selection of a financial account for agiven financial transaction.

Financial Account Information

As mentioned above, financial account information may be one factor usedby a TDFAS system when selecting among a plurality of financial accountsthat could be used to fund a given transaction. Financial accountinformation may include any information relating to a financial account.Financial account information may include, for example, a balance of afinancial account; a credit limit of the financial account; rewardinformation associated with the financial account; account holderinformation on file with a financial institution including names,emails, phone numbers, and addresses; income information associated withthe financial account; liability information including any recurringpayments associated with the financial account, any loans associatedwith the financial account, an amount owed for any existing loan, loanterms for any existing loans, and original loan amount; and one or morefinancial transactions associated with the financial account that eachinclude: a date, a merchant name or transaction description, location,category, and amount.

Transaction Information

Instead of or in addition to financial account information, a TDFASsystem may selecting the financial account to use to fund a giventransaction based on information about the transaction itself. The term“transaction information”, as used herein, refers to any informationabout the specific transaction for which the TDFAS system is selectin afinancial account. For example, the transaction information may indicatethat a transaction occurred at McDonalds in Madrid, Spain. The TDFASsystem may utilize a third-party service to determine that McDonalds isincluded in a ‘dining’ category or may use internal programmable logicto determine that McDonalds is included in a ‘dining’ category.Likewise, a TDFAS system may use internal programmable logic todetermine that ‘Madrid, Spain’ is a different country than an addressassociated with the multi-account payment card that is being used in thetransaction. Consequently, the TDFAS system may identify that thecategory ‘Foreign Spending’ is applicable to the transaction.

Tdfas System Overview

FIG. 1 is a block diagram that illustrates a TDFAS system, according anembodiment. Referring to the embodiment illustrated in FIG. 1 ,financial System 110 includes processing computing device 112, financialcomputing device 114, and banking computing device 116. Each ofprocessing computing device 112, financial computing device 114, andbanking computing device 116 may comprise one or more server computersor other computing devices that execute programmatic instructions tofacilitate financial account selection operations, as further discussedherein.

Financial system 110 includes payment intermediary computer system 118.In one embodiment, payment intermediary computer system 118 is under thecontrol of banking computing device 116. In another embodiment, paymentintermediary computer system 118 is under the control of financialcomputing device 114. In another embodiment, payment intermediarycomputer system 118 is under the control of processing computing device112.

In TDFAS system 100, a transaction is initiated when a multi-accountpayment card 102 is used at the point of sale (POS) system 104.Multi-account payment card 102 may comprise a magnetic stripe card ordigital chip card similar to a credit card. In some embodiments,multi-account payment card 102 may be swiped through a card reader thatis coupled to POS System 104. The card reader may send the informationobtained from multi-account payment card 102 (the “multi-account paymentcard information”) to POS system 104. POS system 104 then transmits themulti-account payment card information to financial System 110 vianetwork 106. POS system 104 may also transmit transaction information.Transaction information may include any information obtained from thePOS 104, such as a designation of goods or services being purchased in atransaction (e.g. “carrots”) or a category of goods or services beingpurchased in a transaction (e.g. “groceries”).

The multi-account payment card information and transaction informationmay be received by processing computing device 112. An example ofprocessing computing device 112 is Wells Fargo Merchant Services.Processing computing device 112, based on the received multi-accountpayment card information, may relay the multi-account payment cardinformation to the appropriate financial computing device 114. Forexample, if multi-account payment card 102 is encoded as a VISA brandedmulti-account payment card, processing computing device 112 relays thereceived information to the financial computing device 114 thatprocesses VISA branded multi-account payment cards. In this example,financial computing device 114 may be VISA’s VisaNet Payment system,which processes payments made using VISA branded multi-account paymentcards.

The multi-account payment card information includes meta-data whichfinancial computing device 114 uses to transmit the multi-accountpayment card information to payment intermediary computer system 118,such as a device ID or IP address. Financial computing device 114 maytransmit multi-account payment card information to TDFAS system 118 forfurther processing.

Payment intermediary computer system 118 receives multi-account paymentcard information and transaction information from financial System 110.Payment information may include any information obtained from amulti-account payment card that was used to initiate a transaction, suchas a multi-account payment card ID. Payment intermediary computer system118 may also query financial account information from database 108.Payment intermediary computer system 118 may obtain financial accountinformation (a) from third party sources or obtain such information, (b)manually from a consumer using computing device 120, and/or (c) anycombination thereof.

Payment intermediary computer system 118 may comprise one or more servercomputers or other computing devices that execute programmaticinstructions to perform financial account selection operations, asfurther discussed herein. Once payment information and transactioninformation are received, payment intermediary computer system 118determines one or more financial accounts that are associated with themulti-account payment card and selects a financial account to use forthe financial transaction. Once a financial account is selected, paymentintermediary computer system 118 sends the selected financial accountinformation to financial system 110. In the embodiment of FIG. 1 , thisincludes sending the selected financial account information to financialcomputing device 114.

The financial devices to which the transaction information and financialaccount information is sent is dictated by the financial account that isselected by the payment intermediary computer system 118. Once received,financial computing device 114 determines that the received financialaccount is managed by banking computing device 116 and sends thetransaction and financial account information to banking computingdevice 116. An example of a banking computing device is server computingoperated by an entity such as Wells Fargo Bank. Banking computing device116 determines the result of payment authorization and sends the resultsof the authorization to POS System 104 to complete the financialtransaction.

Upon completion of the transaction, payment intermediary computer system118 generates a digital receipt, and causes the digital receipt to bedelivered to computing device 120 associated with a consumer (e.g., tothe consumer’s mobile phone). Once a digital receipt is received, aconsumer can optionally perform various actions associated with thetransaction via the digital receipt. The various actions, orinteractions, performed via the digital receipt may include, forexample: providing feedback regarding the selection of a financialaccount; accepting an offer included in the digital receipt 160; andreviewing loyalty reward points awarded as a result of the transaction.

Example Procedure for Transaction-Dependent Account Selection

FIG. 2 is a flowchart illustrating how TDFAS system 100 may performtransaction-dependent financial account selection, according to anembodiment. FIG. 2 and each other flow diagram herein illustrates analgorithm or plan that may be used as a basis for programming one ormore of the functional modules of FIG. 1 that relate to the functionsthat are illustrated in the diagram, using a programming developmentenvironment or programming language that is deemed suitable for thetask. Thus, FIG. 2 and each other flow diagram herein are intended as anillustration at the functional level at which skilled persons, in theart to which this disclosure pertains, communicate with one another todescribe and implement algorithms using programming. The flow diagramsare not intended to illustrate every instruction, method object or substep that would be needed to program every aspect of a working program,but are provided at the high, functional level of illustration that isnormally used at the high level of skill in this art to communicate thebasis of developing working programs. For purposes of illustrating aclear example, FIG. 2 and other flow diagrams are discussed in thecontext of FIG. 1 , but the algorithms of FIG. 2 and the other flowdiagrams also can be implemented in other contexts.

In step 202, transaction information of a financial transaction isreceived. The financial transaction involves a multi-account paymentcard that is associated with a particular multi-account payment card ID.For example, multi-account payment card 102 may be swiped through a cardreader that is coupled to POS system 104. The card reader may send themulti-account payment card information obtained from the multi-accountpayment card along with transaction information of the financialtransaction to POS system 104. POS system 104 then transmits themulti-account payment card information and transaction information tofinancial system 110, which transmits the multi-account payment cardinformation and transaction information to payment intermediary computersystem 118.

In step 204, it is determined that the multi-account payment card ID isassociated with a plurality of financial accounts. Each of the pluralityof financial accounts represents a distinct source of funding, such as acredit card, a debit card, an automatic teller machine (ATM) card, agift card, a credit line, etc. For example, payment intermediarycomputer system 118 may query database 108 to determine the plurality offinancial accounts that are associated with the multi-account paymentcard ID.

In step 206, a first financial account of the plurality of financialaccounts is selected to use in the financial transaction.

General Logic for Selecting Which Account to Use in a Given Transaction

Payment intermediary computer system 118 can make the selection of whichfinancial account to use in a given transaction based on variouscriteria. In general, the selection of a financial account can be madebased on (a) the transaction information received in step 202, and (b)financial account information. As discussed above, transactioninformation may include any information obtained from a POS such as adesignation of goods or services being purchased in a transaction or acategory of goods or service being purchased in a transaction.

Financial account information for a plurality of financial accounts canbe analyzed to determine one or more categories for each financialaccount of the plurality of financial accounts. For example, rewardinformation for a particular financial account may specify that thefinancial account offers 3 points per dollar on travel purchases, 3points per dollar on dining purchases, 1 point per dollar everythingelse, and no foreign transaction fees. Based on the reward informationfor the particular financial account, the categories of ‘Travel’,‘Dining’, ‘Everyday Spending’, and ‘Foreign Spending’ may be associatedwith the particular financial account.

When transaction information is received, such as in step 202, thetransaction information can be analyzed to determine one or morecategories for the transaction. To determine which financial account ofthe plurality of financial accounts to select, the one or morecategories of each financial account of the plurality of financialaccounts can be compared with the one or more categories of thetransaction to identify matching categories. For example, if aparticular financial account of the plurality of financial accountsincludes both ‘Travel’ and ‘Foreign Spending’ categories and thetransaction information also includes both ‘Travel’ and ‘ForeignSpending’, two matching categories are identified, and the particularfinancial account is selected.

In the case where there is not an exact match between categories offinancial accounts, a financial account of the plurality of financialaccounts that has the most matching categories with the one or morecategories of the transaction information is selected. In the case wheretwo or more financial accounts have the same number of matchingcategories with the one or more categories of the transactioninformation, a financial account of two or more financial accounts thathas a best ranking for the matching categories. In some embodiments,financial accounts can be ranked for different categories. Rankings offinancial accounts can be retrieved from a third-party source or can beadded manually by an administrator or consumer. For example, a firstfinancial account may be ranked #2 for ‘Travel’ and #5 for ‘Dining’while a second financial account may be ranked #1 for ‘Travel’ and #6for ‘Dining’. The ranking of categories of financial accounts can beused to select a financial account.

In some embodiments, a financial account associated with one or moregift cards may be selected to use in a given financial transaction ifthe financial transaction is determined to be from an entity thataccepts such gift cards. For gift cards that may be used at a variety ofentities (i.e. stores), this technique is particularly useful. Forexample, if a consumer has a gift card for a particular restaurant groupwhich may be used at a variety of restaurant brands in the group, thecard may be connected to the payment intermediary computer system 118.Based on card metadata, the gift card may be matched with a list ofbrands and individual stores for which the card may be used. When afinancial transaction is initiated, the gift card will be selected if itcan be used at that store. In some cases, a consumer may not even knowthat the card is redeemable at that restaurant, particularly when thecard is branded as or was purchased from another store that was brandedwith a different restaurant brand in the restaurant group.

Financial Metrics for Selecting Which Account to Use in a GivenTransaction

Payment intermediary computer system 118 can calculate several differentmetrics based on financial account information. One health metricrepresents an income-to-spend ratio. A second health metric represents atrajectory of the income-to-spend ratio, which identifies whether theincome-to-spend ratio is consistently headed in a positive or negativedirection. A third health metric measures the variability of thetrajectory of the income-to-spend ratio and the income-to-spend ratioagainst an overall balance of financial accounts. For example, aconsumer with a negative trajectory of the income-to-spend ratiorepresenting a $1000 per month decrease in account balance might have athird health metric in an acceptable range if the total financialaccount balance is $400,000, while a consumer with a total financialaccount balance of $2000 will have a significantly different thirdhealth metric, representing a severe (individualized) financial healthtrajectory. Another payment-related set of metrics measures the impactof the first three on the ability to pay outstanding credit-relatedbills, and makes a determination regarding whether the bills can be paidoff entirely with little impact on the first three metrics.

The above discussed metrics can be used to determine what financialaccount to select. For example, if the trajectory of the income-to-spendratio indicates a negative trajectory and paying off a financial accountwould cause an impact on the third health metric, then a lower interestcard would be preferable, and may be selected to use in a giventransaction. In addition, an alert might be sent to the customer via amobile application to indicate the impact of the purchase on theirfinancial health.

However, if the payment intermediary computer system 118 determines thatpayment-related metrics show that paying off a financial account balancewill have little impact on the third financial health metric, then thedecision about which financial account to charge will be based on thebenefits or rewards associated with the financial accounts. For example,if the metrics indicate that the third financial health metric wouldreach an undesirable threshold if the financial transaction in questionwere to be paid off in full, then the payment intermediary computersystem 118 determines that the charge should be attributed to thefinancial account with the lowest interest rate.

The above discussed metrics and additional metrics can be generated byfinancial account information capture a complete view of financialhealth, and to look for gaps. For example, by reviewing checkingfinancial account transactions and matching them against credit cardfinancial account transactions, the system can tell which financialaccount is typically used for such transactions and track the balance ofa particular account. By reviewing transactions for each credit card,the payment intermediary computer system 118 can generate a metric thatshows the likelihood that a particular card is paid off in full or trackthe typical payment. For example, if a payment is always or nearlyalways (determined against a threshold) for the full amount, then thereis a reasonable confidence that the card will be paid in full in thenext cycle. However, that confidence can be weakened if the balance ispredicted to be greater than in previous months based on current andprojected spending on that card. In this way, each card may have adifferent paid-in-full confidence metric, and the payment intermediarycomputer system 118 may choose to pay with the card that is likely to bepaid in full, if doing so is unlikely to impact the third financialhealth metric discussed above. These situations may arise, for example,when a consumer has one credit card set up for auto-pay in full, whileanother is set up to automatically pay the minimum balance. By detectingthis, the payment intermediary computer system 118 can make a suggestionto the consumer that they increase the amount to be automatically paidon the latter credit card account.

These metrics can be calculated on a regular basis to be presented to aconsumer or can be used to present other information or suggestions tothe consumer. For example, showing a consumer that their spendtrajectory is 50% of their income (i.e. the second health metric) canindicate that the consumer may benefit from considering investmentoptions or moving money into an interest-bearing account. Using thefinancial account information, the payment intermediary computer system118 can identify which accounts are interest bearing. The paymentintermediary computer system 118 can then make suggestions. Using thethird health metric, the payment intermediary computer system 118 canshow consumers the deviation in their spending habits, even if theirgeneral financial health is good. These metrics can even be used to setalerts, with preferences for each user. Showing users metrics associatedwith each financial account can help users identify spending and paymenthabits on a per-account basis, which may be useful to help consumersavoid underpaying or to identify cards for which too many auto-bill-payconnections have been attached.

Machine Learning for Selection

In some embodiments, supervised learning may be used in order togenerate a machine learning model. Not depicted in FIG. 1 , creating asupervised machine learning model may include receiving transactioninformation, financial account information, and an indication of aselection of a financial account based on the transaction informationand financial account information. The supervised machine learning modelcan then be trained using the transaction information, financial accountinformation, and an indication of a selection of a financial accountbased on the transaction information and financial account informationas the training data. The supervised machine learning model can then beused to select a financial account of the plurality of financialaccounts for use in a given financial transaction. If the supervisedmachine learning model indicates that a particular financial account ofthe plurality of financial accounts should be used for a transaction,the particular financial account can be selected for use in a givenfinancial transaction. Examples of supervised machine learningalgorithms that could be used include, but are not limited to, SupportVector Machines, linear regression, logistic regression, neuralnetworks, and nearest neighbor methods.

For example, historical transaction records may be stored that includehistorical records of previous transactions and corresponding financialaccount selections. Each record may include: transaction information fora particular transaction, financial account information for theplurality of financial accounts at a time that the particular financialtransaction was received, and an indication of which financial accountwas selected for the particular transaction. To train the supervisedmachine learning model, the transaction information for the particulartransaction and the financial account information for the plurality offinancial accounts at a time that the particular financial transactionwas received may be used an input while the indication of whichfinancial account was selected for the particular transaction may beused as output. As discussed above, the supervised machine learning canbe used in isolation to select a financial account of the plurality offinancial accounts for use in a given financial transaction or can beused in combination with other selection techniques discussed herein toselect a financial account of the plurality of financial accounts foruse in a given financial transaction.

In some embodiments, an unsupervised machine learning model may be usedto detect transaction anomalies. For example, the unsupervised machinelearning model may take in one or more financial transactions associatedwith each financial account of the plurality of financial accounts anddetect when a new financial transaction is unlike previous financialtransactions from the plurality of financial accounts. Such a satisfiedcondition for the unsupervised machine learning model may result in analert being triggered and transmitted to a consumer associated withcomputing device 120. In some cases, detection of a transaction anomalywill result in a selection of a particular financial account that isidentified as being designated for anomaly transactions. Examples ofunsupervised machine learning algorithms that may be used includek-means clustering, mixture models, hierarchical clustering, NeuralNetworks, autoencoders, Deep Belief Nets, Hebbian Learning, GenerativeAdversarial Networks, and self-organizing maps.

For example, if a consumer purchases a motorcycle for $30,000 and thesame consumer has only made purchases in the past for $1,000 or less,the unsupervised machine model may detect an anomaly transaction.

In some embodiments, transaction anomalies can be detected bydetermining that an amount of a received financial transaction isoutside a threshold value of one or more amounts associated withfinancial transactions from the plurality of financial accounts. Forexample, if a calculated mean (i.e. average) amount for financialtransactions of the plurality of financial accounts is $100 and athreshold value is set to $300, any financial transaction with an amountgreater than $400 is detected as a transaction anomaly.

In some embodiments, once a transaction anomaly is detected for afinancial transaction, it may be determined that, based on financialaccount information associated with the plurality of financial accounts,that an amount associated with the financial transaction is unlikely tobe paid off within a specific timeframe. In response to such adetermination, a financial account of the plurality of financialaccounts with the lowest interest rate may be selected. Similarly, itmay be determined that, based on financial account informationassociated with the plurality of financial accounts, that an amountassociated with the financial transaction is likely to be paid offwithin a specific timeframe. In response to such a determination, afinancial account of the plurality of financial accounts that awards themost points for a category associated with the financial transaction maybe selected.

In some embodiments, output of a trained machine learning model may becombined with other financial account selection techniques discussedherein to select a financial account for use in a given financialtransaction. For example, an output of a trained supervised machinelearning model that selects a particular financial account may beweighted and combined with an output of another financial accountselection technique to select a financial account for use in a givenfinancial transaction.

As a specific example, suppose a trained supervised machined learningmodel produces a first output that specifies that a first financialaccount should be selected for use in a given financial transaction.Also suppose that by comparing categories of financial accountinformation and transaction information, as discussed herein, a secondoutput is generated that indicates that a second financial accountshould be selected for use in a given financial transaction. Because thefirst and second outputs differ, the ultimate selection of a financialaccount will depend on how much weight is given to the first output ofthe supervised machine learning model. Such weightings can be configuredby a consumer and may be static or dynamic. The weights may be static ina sense that when a weight for an output of a machine learning model isselected, the weight will not change unless modified by a consumer. Theweights may be dynamic in a sense that the weight for an output of aparticular machine learning model may increase over time as morefeedback information is used to continuously train the particularmachine learning model. Thus, in the case of dynamic weighting, as theparticular machine learning model becomes more accurate over time due toreceiving more and more feedback information, the output of theparticular machine learning model is given more weight and thus has agreater influence on selecting a financial account, in combination withother financial account selection techniques, for use in a givenfinancial transaction.

Consumer Preference Information

In some embodiments, the selection of a financial account can be made bythe TDFAS system based on consumer preference information, in additionto the transaction information and financial account information.Consumer preference information can be provided by a consumer associatedwith the financial account by the payment intermediary computer system118 prompting the consumer to select preferences via computing device,such as a mobile computing device. Consumer preference information mayspecify selections of financial accounts that should be used for certaincategories of transactions. For example, consumer preference informationmay include a preference that indicates that a particular financialaccount should be used for all travel related purchases. As anotherexample, consumer preference information may include a preference thatindicates that a particular financial account should be used for alldining purchases. As another example, consumer preference informationmay include a preference that indicates that a particular financialaccount should be used for all transaction anomalies. As anotherexample, consumer preference information may indicate that a priorityfor the consumer is to accumulate cash back points.

Consumer preference information may also specify selections of financialaccount information of one or more financial accounts that should be orshould not be included when selecting a financial account. For example,consumer preference information may specify that liability informationshould not be included as a basis for selecting a financial account. Asanother example, consumer preference information may specify that incomeinformation should not be included as a basis for selecting a financialaccount.

Additionally, in some embodiments, consumers can set goals, andprioritize those goals in consumer preference information. For example,if a consumer has a goal to maximize cash availability in a financialaccount, then gift card use will be prioritized, with lower interestrate cards after that. If a consumer has a certain amount of points theyare attempting to acquire for travel use, then points may take priorityuntil that threshold is reached.

In other embodiments, consumers may set up different consumer preferenceprofiles based on the context of their spending. For example, a consumermay use a mobile app to “turn on” a business reimbursement spendingprofile, which includes preferences indicating that all spending shouldbe charged to a particular financial account to maximize points.Reminders or notifications can be used to remind the consumer to paythat financial account in full once the reimbursement is detected tohave arrived in the consumer’s financial account. Reimbursementdetection can be executed by recording the amount spent during that useof a particular consumer preference profile, and searching for anon-typical (not normal payroll) deposit in the consumer’s correspondingfinancial account.

Consent Alerts

In some embodiments, once a financial account is selected should beselected for use in a financial transaction but before the financialtransaction is charged to a financial account, payment intermediarycomputer system 118 may generate and cause the display of an interfacethat prompts a consumer to consent to the selection a financial accountof the plurality of financial accounts for use in the financialtransaction at a computing device associated with the consumer. Forexample, a prompt may be delivered to the consumer that states “YourChase Reserve financial account has been selected for this transaction.Would you like to use your Chase Reserve financial account for thistransaction?” In the case that the consumer consents, the selectedfinancial account is used in step 208. In the case the consumer does notconsent, the consumer can select another financial account of theplurality of financial accounts to proceed with. In some situations, afinancial transaction may time out before a user consents to or selectsa financial account. In this scenario, no financial transaction ischarged to a financial account.

In step 208, the financial transaction is charged to the first financialaccount. Charging the financial transaction to the first financialaccount may comprise causing the financial transaction to be charged tothe first financial account. In context of FIG. 1 , causing thefinancial transaction to be charged to the first financial accountincludes payment intermediary computer system 118 sending the financialtransaction and financial account information associated with the firstfinancial account to processing computing device 112. Processingcomputing device 112 sends the financial transaction and financialaccount information associated with the first financial account tofinancial computing device 114. Financial computing device 114determines that the received financial account is managed by bankingcomputing device 116 and sends the transaction and financial accountinformation to banking computing device 116. Banking computing device116 determines the result of a payment authorization process and sendsthe results of the authorization process to POS System 104 to completethe financial transaction.

In step 210, a digital receipt indicating that the financial transactionwas charged to the first financial account is generated based on thefinancial transaction being charged to the first financial account. Forexample, payment intermediary computer payment intermediary computersystem generates the digital receipt based on the financial transactionbeing charged to the first financial account. The digital receipt maythen be transmitted to computing device 120 that is associated with aconsumer that initiated the financial transaction.

Explanatory Data

In some embodiments, the digital receipt includes explanatoryinformation that provides an explanation of why the first financialaccount of the plurality of financial account was selected for use in agiven financial transaction. The payment intermediary computer system118 generates explanation information about why the financial accountselection was made. For example, the explanatory information mayindicate that a particular financial account was selected for use in agiven financial transaction because the transaction is related to traveland the particular financial account offers the most reward points fortravel out of the plurality of financial accounts. As another example,the explanatory information may indicate that a particular financialaccount was selected for use in a given financial transaction becausethe transaction was identified as a transaction anomaly and theparticular financial account offers lowest monthly interest rate out ofthe plurality of financial accounts.

Feedback

In some embodiments, the digital receipt includes an interface thatallows a consumer to provide feedback information regarding theselection of the first financial account for use in the financialtransaction. For example, a consumer may provide feedback that theselection of the first financial account for use in the financialtransaction was preferred. As another example, a consumer may providefeedback that the selection of the first financial account for use inthe financial transaction was not preferred, and that a selection of adifferent financial account of the plurality of financial accounts foruse in the financial transaction would have been preferred.

The feedback information associated with each transaction can be used totrain a supervised machine learning model in additional stages as it isreceived. As the supervised machine learning model receives morefeedback information to train on, the accuracy of the output selectionsof financial accounts from the supervised machine learning model can beimproved incrementally with each transaction over time.

Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer Systems, portable computer Systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 3 is a block diagram that illustrates a computerSystem 300 upon which an embodiment of the invention may be implemented.Computer System 300 includes a bus 302 or other communication mechanismfor communicating information, and a hardware processor 304 coupled withbus 302 for processing information. Hardware processor 304 may be, forexample, a general purpose microprocessor.

Computer System 300 also includes a main memory 306, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 302for storing information and instructions to be executed by processor304. Main memory 306 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 304. Such instructions, when stored innon-transitory storage media accessible to processor 304, rendercomputer System 300 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer System 300 further includes a read only memory (ROM) 308 orother static storage device coupled to bus 302 for storing staticinformation and instructions for processor 304. A storage device 310,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 302 for storing information and instructions.

Computer System 300 may be coupled via bus 302 to a display 312, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 314, including alphanumeric and other keys, is coupledto bus 302 for communicating information and command selections toprocessor 304. Another type of user input device is cursor control 316,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 304 and forcontrolling cursor movement on display 312. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer System 300 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer System causes orprograms computer System 300 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computerSystem 300 in response to processor 304 executing one or more sequencesof one or more instructions contained in main memory 306. Suchinstructions may be read into main memory 306 from another storagemedium, such as storage device 310. Execution of the sequences ofinstructions contained in main memory 306 causes processor 304 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 310. Volatile media includes dynamic memory, such asmain memory 306. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 302. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 304 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer System 300 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 302. Bus 302 carries the data tomain memory 306, from which processor 304 retrieves and executes theinstructions. The instructions received by main memory 306 mayoptionally be stored on storage device 310 either before or afterexecution by processor 304.

Computer System 300 also includes a communication interface 318 coupledto bus 302. Communication interface 318 provides a two-way datacommunication coupling to a network link 320 that is connected to alocal network 322. For example, communication interface 318 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 318 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 318sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 320 typically provides data communication through one ormore networks to other data devices. For example, network link 320 mayprovide a connection through local network 322 to a host computer 324 orto data equipment operated by an Internet Service Provider (ISP) 326.ISP 326 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 328. Local network 322 and Internet 328 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 320and through communication interface 318, which carry the digital data toand from computer System 300, are example forms of transmission media.

Computer System 300 can send messages and receive data, includingprogram code, through the network(s), network link 320 and communicationinterface 318. In the Internet example, a server 330 might transmit arequested code for an application program through Internet 328, ISP 326,local network 322 and communication interface 318.

The received code may be executed by processor 304 as it is received,and/or stored in storage device 310, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A computer-implemented method for selecting afinancial account for a financial transaction, comprising: inputtinginto an unsupervised machine learning model financial accountinformation of a plurality of financial accounts of a consumer includinginformation about previous financial transactions performed by theconsumer that initiated the financial transaction; receiving transactioninformation of the financial transaction at a payment intermediarycomputer system, the financial transaction involving a multi-accountpayment card associated with a multi-account payment card identification(ID) of the consumer; determining, by the payment intermediary computersystem, that the multi-account payment card ID is associated with theplurality of financial accounts of the consumer; wherein each of theplurality of financial accounts is associated with a distinct paymentsource; wherein the plurality of financial accounts includes a firstfinancial account designated for use with anomaly transactions; whereinan anomaly transaction is a financial transaction of the consumer thatis determined by the unsupervised machine learning model to be unlikethe previous financial transactions performed by the consumer using theplurality of financial accounts; in response to determining that themulti-account payment card ID is associated with the plurality offinancial accounts, selecting, by the intermediary computer system, thefirst financial account of the plurality of financial accounts based, atleast in part, on: inputting into the unsupervised machine learningmodel information about the financial transaction; determining, usingthe unsupervised machine learning model, that the financial transactionis an anomaly transaction because the financial transaction is unlikethe previous financial transactions performed by the consumer, using theplurality of financial accounts, that were input into the unsupervisedmachine learning model; and based on the unsupervised machine learningmodel determining that the financial transaction is an anomalytransaction, selecting the first financial account of the plurality offinancial accounts; and causing the intermediary computer system to usethe first account to complete the financial transaction.
 2. The methodof claim 1, wherein: receiving transaction information of the financialtransaction comprises receiving the transaction information from a firstfinancial computing device associated with the multi-account paymentcard; and the computing device associated with the first financialaccount is a second financial computing device different from the firstfinancial computing device.
 3. The method of claim 1, wherein: receivingtransaction information of the financial transaction comprises receivingthe transaction information from a first financial computing deviceassociated with the multi-account payment card; and the computing deviceassociated with the first financial account is a banking computingdevice different from the first financial computing device.
 4. Themethod of claim 1, wherein: the information about the previous financialtransactions performed by the consumer includes amounts of the previousfinancial transactions; the information about the financial transactionincludes an amount of the financial transaction; and the unsupervisedmachine learning model determines the financial transaction is ananomaly transaction by determining that the amount of the financialtransaction is outside a threshold value of a mean amount for theprevious financial transactions.
 5. The method of claim 1, wherein theunsupervised machine learning model uses k-means clustering, mixturemodels, hierarchical clustering, Neural Networks, autoencoders, DeepBelief Nets, Hebbian Learning, Generative Adversarial Networks, orself-organizing maps.
 6. The method of claim 1, wherein determining thatthe multi-account payment card ID is associated with a plurality offinancial accounts of the consumer comprises querying a database todetermine the plurality of financial accounts associated with themulti-account payment card ID.
 7. The method of claim 1, furthercomprising: before causing the financial transaction to be charged tothe first financial account, generating and displaying, at a computingdevice associated with the consumer, an interface that allows theconsumer to provide consent to charge the financial transaction to thefirst financial account.
 8. The method of claim 1, further comprising:generating, based on the financial transaction being charged to thefirst financial account, a digital receipt indicating that the financialtransaction was charged to the first financial account; and transmittingthe digital receipt to a computing device associated with the consumer.9. The method of claim 8, wherein the digital receipt includesexplanatory information that provides an explanation of why the firstfinancial account of the plurality of financial accounts was selected.10. The method of claim 8, wherein the digital receipt includes aninterface that allows a consumer to provide feedback informationregarding the selection of the first financial account.
 11. One or morenon-transitory computer-readable media storing instructions which, whenexecuted by one or more processors, cause: inputting into anunsupervised machine learning model financial account information of aplurality of financial accounts of a consumer including informationabout previous financial transactions performed by the consumer thatinitiated the financial transaction; receiving transaction informationof the financial transaction at a payment intermediary computer system,the financial transaction involving a multi-account payment cardassociated with a multi-account payment card identification (ID) of theconsumer; determining, by the payment intermediary computer system, thatthe multi-account payment card ID is associated with the plurality offinancial accounts of the consumer; wherein each of the plurality offinancial accounts is associated with a distinct payment source; whereinthe plurality of financial accounts includes a first financial accountdesignated for use with anomaly transactions; wherein an anomalytransaction is a financial transaction of the consumer that isdetermined by the unsupervised machine learning model to be unlike theprevious financial transactions performed by the consumer using theplurality of financial accounts; in response to determining that themulti-account payment card ID is associated with the plurality offinancial accounts, selecting, by the intermediary computer system, thefirst financial account of the plurality of financial accounts based, atleast in part, on: inputting into the unsupervised machine learningmodel information about the financial transaction; determining, usingthe unsupervised machine learning model, that the financial transactionis an anomaly transaction because the financial transaction is unlikethe previous financial transactions performed by the consumer, using theplurality of financial accounts, that were input into the unsupervisedmachine learning model; and based on the unsupervised machine learningmodel determining that the financial transaction is an anomalytransaction, selecting the first financial account of the plurality offinancial accounts; and causing the intermediary computer system to usethe first account to complete the financial transaction.
 12. The one ormore non-transitory computer-readable media of claim 11, wherein:receiving transaction information of the financial transaction comprisesreceiving the transaction information from a first financial computingdevice associated with the multi-account payment card; and the computingdevice associated with the first financial account is a second financialcomputing device different from the first financial computing device.13. The one or more non-transitory computer-readable media of claim 11,wherein: receiving transaction information of the financial transactioncomprises receiving the transaction information from a first financialcomputing device associated with the multi-account payment card; and thecomputing device associated with the first financial account is abanking computing device different from the first financial computingdevice.
 14. The one or more non-transitory computer-readable media ofclaim 11, wherein: the information about the previous financialtransactions performed by the consumer includes amounts of the previousfinancial transactions; the information about the financial transactionincludes an amount of the financial transaction; and the unsupervisedmachine learning model determines the financial transaction is ananomaly transaction by determining that the amount of the financialtransaction is outside a threshold value of a mean amount for theprevious financial transactions.
 15. The one or more non-transitorycomputer-readable media of claim 11, wherein the unsupervised machinelearning model uses k-means clustering, mixture models, hierarchicalclustering, Neural Networks, autoencoders, Deep Belief Nets, HebbianLearning, Generative Adversarial Networks, or self-organizing maps. 16.The one or more non-transitory computer-readable media of claim 11,wherein determining that the multi-account payment card ID is associatedwith a plurality of financial accounts of the consumer comprisesquerying a database to determine the plurality of financial accountsassociated with the multi-account payment card ID.
 17. The one or morenon-transitory computer-readable media of claim 11, further comprising:before causing the financial transaction to be charged to the firstfinancial account, generating and displaying, at a computing deviceassociated with the consumer, an interface that allows the consumer toprovide consent to charge the financial transaction to the firstfinancial account.
 18. The one or more non-transitory computer-readablemedia of claim 11, further comprising: generating, based on thefinancial transaction being charged to the first financial account, adigital receipt indicating that the financial transaction was charged tothe first financial account; and transmitting the digital receipt to acomputing device associated with the consumer.
 19. The one or morenon-transitory computer-readable media of claim 18, wherein the digitalreceipt includes explanatory information that provides an explanation ofwhy the first financial account of the plurality of financial accountswas selected.
 20. The one or more non-transitory computer-readable mediaof claim 18, wherein the digital receipt includes an interface thatallows a consumer to provide feedback information regarding theselection of the first financial account.